Calculation of quality of wlan access point characterization for use in a wlan positioning system

ABSTRACT

Methods and systems for classifying WLAN access points according to the quality of estimation of characteristics of the WLAN access points are provided. The classifications may be used to scale a reference database and quantify an expected error of estimation of the characteristics of the access points. WLAN access points may be classified based on their impact on a user&#39;s position, speed of travel, and direction of travel estimation accuracy in a WLAN positioning system. A method for determining a quality of estimation of characteristics of a Wi-Fi access point comprises a Wi-Fi enabled scanning device measuring a number of received signal strength (RSS) samples of the Wi-Fi signal transmitted by the Wi-Fi access point. A total distance traveled by the Wi-Fi enabled scanning device while measuring the number of RSS samples is estimated and used to estimate the quality of estimation of characteristics of the Wi-Fi access point.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority under 35U.S.C. § 120 to U.S. patent application Ser. No. 11/430,224, filed onMay 8, 2006, entitled Calculation of Quality of WLAN Access PointCharacterization for Use in a WLAN Positioning System, the entirecontents of which are incorporated herein by reference.

This application is related to the following patent applications:

-   -   U.S. patent application Ser. No. 11/430,079, filed May 8, 2006,        entitled “Estimation Of Speed and Direction of Travel In A WLAN        Positioning System”;    -   U.S. patent application Ser. No. 11/429,862, filed May 8, 2006,        entitled “Estimation of Speed of Travel Using the Dynamic Signal        Strength Variation of Multiple WLAN Access Points”;    -   U.S. patent application Ser. No. 11/430,064, filed May 8, 2006,        entitled “Estimation of Speed and Direction of Travel In A WLAN        Location Based Service Using Multiple Position Estimations”; and    -   U.S. patent application Ser. No. 11/430,222, filed May 8, 2006,        entitled “Estimation of Position Using WLAN Access Point Radio        Propagation Characteristics in a WLAN Positioning System”.

the contents of which are hereby incorporated by reference.

BACKGROUND

1. Field of the Invention

The invention generally relates to positioning systems and, morespecifically, to methods and systems of classifying WLAN access pointsin a WLAN positioning system. The invention further relates tocalculating the quality of estimation of characteristics of the WLANaccess points and scaling a reference database accordingly.

2. Discussion of Related Art

Position determination is the main component of navigation systems andany Location Based Services (LBS). Proliferation of WLAN access pointsin recent years created a blanket of WLAN radio waves everywhere.Therefore, almost in any place, there is a great possibility ofdetecting WLAN radio waves, especially in urban areas. The exponentialgrowth of WLAN, and the fact that they can be found almost everywhere,initiated an idea of leveraging them for a metropolitan positioningsystem for indoor and outdoor areas. In a metropolitan WLAN positioningsystem, location of WLAN access points are used as reference points, andthe Received Signal Strength (RSS) of a WLAN access point is used as anindicator of a distance of an end user from the WLAN access points thatthe user detects at any time. By knowing the distance of the end userfrom WLAN access points, location of the end user can be determined.Translating receiver Receive Signal Strength to distance relies onassuming a specific radio channel model. Ideally, if the radio channelmodel was exactly known, the exact distance of the end user to WLANaccess points could be found.

Outdoor and indoor WLAN based positioning systems have been explored bycouple of research labs, but none of them included speed and bearingestimation in their system. The most important research efforts in thisarea have been conducted by PlaceLab (www.placelab.com, a projectsponsored by Microsoft and Intel), University of California San DiegoActiveCampus project (ActiveCampus—Sustaining Educational Communitiesthrough Mobile Technology, technical report #CS2002-0714), and the MITcampus wide location system, and it was evaluated through several smallprojects at Dartmouth college (e.g., M. Kim, J. J. Fielding, and D.Kotz, “Risks of using AP locations discovered through war driving”).

There have been a number of commercial offerings of Wi-Fi locationsystems targeted at indoor positioning. (See, e.g., KavithaMuthukrishnan, Maria Lijding, Paul Having a, Towards Smart Surroundings:Enabling Techniques and Technologies for Localization, Proceedings ofthe International Workshop on Location and Context-Awareness (LoCA 2005)at Pervasive 2005, May 2005, and Hazas, M., Scott, J., Krumm, J.:Location-Aware Computing Comes of Age. IEEE Computer, 37(2):95-97,February 2004 005, Pa005, Pages 350-362.) These systems are designed toaddress asset and people tracking within a controlled environment like acorporate campus, a hospital facility or a shipping yard. The classicexample is having a system that can monitor the exact location of thecrash cart within the hospital so that when there is a cardiac arrestthe hospital staff doesn't waste time locating the device. The accuracyrequirements for these use cases are very demanding typically callingfor 1-3 meter accuracy.

These systems use a variety of techniques to fine tune their accuracyincluding conducting detailed site surveys of every square foot of thecampus to measure radio signal propagation. They also require a constantnetwork connection so that the access point and the client radio canexchange synchronization information similar to how A-GPS works. Whilethese systems are becoming more reliable for indoor use cases, they areineffective in any wide-area deployment. It is impossible to conduct thekind of detailed site survey required across an entire city and there isno way to rely on a constant communication channel with 802.11 accesspoints across an entire metropolitan area to the extent required bythese systems. Most importantly outdoor radio propagation isfundamentally different than indoor radio propagation rendering theseindoor positioning algorithms almost useless in a wide-area scenario.The required accuracy of indoor WLAN based positioning systems, makes ithard to use radio channel modeling and it is considered as a researchtopic in that domain. In addition, none of the WLAN based positioningsystems to date have distinguished between access points, and currentmethods treat all WLAN access points the same.

FIG. 1 depicts a Wi-Fi positioning system (WPS). The positioning systemincludes positioning software [103] that resides on a computing device[101]. Throughout a particular coverage area there are fixed wirelessaccess points [102] that broadcast information using control/commonchannel broadcast signals. The client device monitors the broadcastsignal or requests its transmission via a probe request. Each accesspoint contains a unique hardware identifier known as a MAC address. Theclient positioning software receives signal beacons from the 802.11access points in range and calculates the geographic location of thecomputing device using characteristics from the signal beacons. Thosecharacteristics include the unique identifier of the 802.11 accesspoint, known as the MAC address, and the strengths of the signalreaching the client device. The client software compares the observed802.11 access points with those in its reference database [104] ofaccess points, which may or may not reside on the device as well. Thereference database contains the calculated geographic locations andpower profile of all the access points the gathering system hascollected. The power profile may be generated from a collection ofreadings that represent the power of the signal from various locations.Using these known locations, the client software calculates the relativeposition of the user device [101] and determines its geographiccoordinates in the form of latitude and longitude readings. Thosereadings are then fed to location-based applications such as friendfinders, local search web sites, fleet management systems and E911services.

SUMMARY

The invention provides methods and systems for classifying WLAN accesspoints according to the quality of estimation of characteristics of theWLAN access points. The classifications may be used to scale a referencedatabase and quantify an expected error of estimation of thecharacteristics of the WLAN access points. Under one aspect of theinvention, WLAN access points are classified based on their impact on auser's position, speed of travel, and direction of travel estimationaccuracy in a WLAN positioning system.

Under another aspect of the invention, a positioning system has aplurality of Wi-Fi access points in a target area. A method fordetermining a quality of estimation of characteristics of a Wi-Fi accesspoint comprises a Wi-Fi enabled scanning device receiving and measuringa number of received signal strength (RSS) samples of the a Wi-Fi signaltransmitted by the Wi-Fi access point. A total distance traveled by theWi-Fi enabled scanning device while measuring the number of RSS samplesis estimated. The quality of estimation of characteristics of the Wi-Fiaccess point is estimated using the total distance traveled by the Wi-Fienabled scanning device.

Under another aspect of the invention, the characteristics of the Wi-Fiaccess point include at least one of geographic location of the Wi-Fiaccess point and radio propagation characteristics of the Wi-Fi accesspoint.

Under another aspect of the invention, a positioning system has aplurality of Wi-Fi access points in a target area. A method fordetermining a quality of estimation of characteristics of a Wi-Fi accesspoint comprises a Wi-Fi enabled scanning device receiving and measuringa number of received signal strength (RSS) samples of the Wi-Fi signaltransmitted by the Wi-Fi access point. A corresponding speed of travelof the Wi-Fi enabled scanning device is associated with each RSS sample.A corresponding period of time of scanning is associated with each RSSsample. A confidence factor is determined using the sum of each of thespeeds of travel of the Wi-Fi enabled scanning device weighted by thecorresponding period of time of scanning for each RSS sample. Thequality of estimation is determined using the confidence factor.

Under another aspect of the invention, a positioning system has aplurality of Wi-Fi access points in a target area. A method fordetermining a quality of estimation of characteristics of a Wi-Fi accesspoint comprises a Wi-Fi enabled scanning device receiving and measuringa number of received signal strength (RSS) samples of the Wi-Fi signaltransmitted by the Wi-Fi access point. A corresponding speed of travelof the Wi-Fi enabled scanning device is associated with each RSS sample.A corresponding period of time of scanning is associated with each RSSsample. A confidence factor is determined using the number of RSSsamples and the speeds of travel of the Wi-Fi enabled scanning deviceweighted by the corresponding period of time of scanning for each RSSsample. The quality of estimation is determined using the confidencefactor.

Under another aspect of the invention, a method for estimating theposition of a Wi-Fi enabled device comprises identifying Wi-Fi accesspoints within range of the Wi-Fi enabled device. Calculated locationsand quality of estimation values corresponding to the identified Wi-Fiaccess points are retrieved from a reference database. The position ofthe Wi-Fi enabled device is estimated using the calculated locations andquality of estimation values.

Under another aspect of the invention, at least one quality ofestimation value corresponding to a Wi-Fi access points determines aweight assigned to a calculated location of the corresponding Wi-Fiaccess point for use in estimating the position of the Wi-Fi enableddevice.

Under another aspect of the invention, a calculated locationcorresponding to a Wi-Fi access points is not used in estimating theposition of the Wi-Fi enabled device if a quality of estimation valuecorresponding to the Wi-Fi access point is below a threshold.

BRIEF DESCRIPTION OF DRAWINGS

In the drawings,

FIG. 1 depicts certain embodiments of a Wi-Fi positioning system;

FIG. 2 depicts an example of an access point with a relatively smallnumber of RSS samples;

FIG. 3 depicts an example of an access point with a relatively largenumber of RSS samples; and

FIG. 4 depicts an example of the impact of total distance of travel of aWi-Fi enabled scanning device on quality of estimation of WLAN accesspoint characteristics.

DETAILED DESCRIPTION

Embodiments of the invention provide methods and systems to define aquality metric for each WLAN access point (AP) in a WLAN basedpositioning system. The quality metric of a WLAN access point may beused as an indicator of the expected error of estimation of position,speed of travel, and direction of travel of a user based on that WLANaccess point. Quantifying expected error of estimation based on a givenWLAN access point can be used to increase the accuracy of overallestimation by giving more weight to more reliable WLAN access points,and it can also be used to quantify expected error of the finalestimation of position, speed of travel and direction of travel byconsidering the quality of an aggregate of the WLAN access points inrange.

Embodiments of the present invention build on techniques, systems andmethods disclosed in earlier filed applications, including but notlimited to U.S. patent application Ser. No. 11/261,848, entitledLocation Beacon Database, U.S. patent application Ser. No. 11/261,898,entitled Server for Updating Location Beacon Database, U.S. patentapplication Ser. No. 11/261,987, entitled Method and System for Buildinga Location Beacon Database, and U.S. patent application Ser. No.11/261,988, entitled Location-Based Services that Choose LocationAlgorithms Based on Number of Detected Access Points Within Range ofUser Device, all filed on Oct. 28, 2005, the contents of which arehereby incorporated by reference in its entirety. Those applicationstaught specific ways to gather high quality location data for Wi-Fiaccess points so that such data may be used in location based servicesto determine the geographic position of a Wi-Fi-enabled device utilizingsuch services and techniques of using said location data to estimate theposition of a system user. The present techniques, however, are notlimited to systems and methods disclosed in the incorporated patentapplications. Thus, while reference to such systems and applications maybe helpful, it is not believed necessary to understand the presentembodiments or inventions.

Because the location of users in a WLAN positioning system arecalculated with reference to the location of public and private WLANaccess points, any inaccuracy in associated parameters of an accesspoint (AP), for example the geographic location of access point,directly impacts the accuracy of position estimation of the users.Aspects of this invention include a systematic method to classify orquantify the quality of WLAN access points. Also, aspects of theinvention can be used to scale a reference database of WLAN accesspoints. Embodiments can be used to quantify the expected error ofresults of calculations using a given WLAN access point. WLAN accesspoints may then be classified based on their level of accuracy. Theknowledge of the accuracy level of the WLAN characteristics can be used,for example, in estimation equations to increase the accuracy of theestimation by using only relatively high quality access points orweighting access points based on their quality.

Characteristics of a WLAN access point, such as its geographic locationor radio propagation characteristics, may be estimated by using a Wi-Fienabled scanning device to collect Received Signal Strength (RSS)samples occurring at corresponding positions. For example, thetechniques disclosed in the applications incorporated above may be used.

The total number of samples collected by the scanning device whencollecting RSS samples for a given WLAN access point may be used tocalculate the expected error of estimation of characteristics for thatWLAN access point. The samples are weighted according to the speed ofthe scanning device when collecting RSS samples. The number of RSSsamples weighted according to the speed of the scanning device at thetime of RSS sampling may be used as a surrogate for the ratio of thenumber of samples to the coverage area of the an access point.

FIG. 2 depicts an example of a WLAN access point [201] having arelatively low quality of characterization because the number of RSSsamples [202] is relatively low. In contrast, FIG. 3 depicts an exampleof a WLAN access point [301] having a relatively high quality ofcharacterization because the number of RSS samples [302] is relativelyhigh, resulting in relatively high accuracy of estimation of WLAN accesspoint characteristics.

FIG. 4 depicts the impact of the speed of the scanning device on thetotal distance traveled by the scanning device while collecting RSSsamples, in which two WLAN access points [401] and [403] with equalnumbers of RSS samples [402] and [404] are presented. During thecollection of the RSS samples [404] for WLAN access point [403], thescanning device was traveling at a higher speed than when the scanningdevice was collecting RSS samples [402] for WLAN access point [401].Where the RSS samples [402] and RSS samples [404] were collected overthe same amount of time, the RSS samples [404] cover a greater totaldistance than the RSS samples [402]. Thus, the RSS samples [404] are abetter indicator of the characteristics, for example the power profile,of the WLAN access point [403] as compared to WLAN access point [401].

Under other embodiments of the invention, the quality of estimation ofcharacteristics of a WLAN access point is quantified. A WLAN accesspoint's geographic location and its radio propagation characteristicsare estimated based on the RSS samples in its coverage area. The numberof RSS samples which are used for estimation of characteristics of anWLAN access point directly impacts the accuracy of estimation. If thenumber of RSS samples of a WLAN access point is relatively low, theerror of estimation of geographic location of the WLAN access point andestimation of its radio propagation characteristics is relatively high.Therefore, WLAN access points with relatively small numbers of RSSsamples can be considered to have relatively low reliability when usedin a WLAN based positioning system. On the other hand, WLAN accesspoints with relatively high numbers of RSS samples can be consideredrelatively high reliability WLAN access points. Under one exampleembodiment, the number of RSS samples can be used to quantify theexpected accuracy of position estimation based on the WLAN accesspoints. Since the expected accuracy of position estimation of differentWLAN access points is different, the estimation based on them can alsobe weighted according to their expected error.

In the process of scanning, the speed of the scanning device is notconstant. The scanning device might stop for a while or it might movefast along highways. As a result, the same number of RSS samples maycover different geographical areas, as explained in connection with FIG.4 above. The geographical area that a given number of RSS samples coversis the speed of the scanning device times the period of scanning.Therefore, assuming a constant scanning period, the value of theabsolute number of samples is weighted according to the speed ofscanning device at time of scanning. The speed of the scanning device atthe time of RSS sampling can be collected, for example, from a GPS or itcan be derived from GPS position over time. The GPS velocity estimationis very accurate because it is based on Doppler frequency of measurementof the GPS received signal, but velocity calculation based on GPSposition over time is a rough estimate of the velocity.

If the estimation of the speed of the scanning device at the time ofscanning is known and the total number of scanned RSS samples is denotedby N, a confidence factor, denoted by CFn, is calculated as follows:

${CFn} = {\sum\limits_{i = 1}^{N}{f\left( {V_{i}T_{i}} \right)}}$

in which V_(i) is the speed of the scanning device and T_(i) is theperiod of scanning at the time of taking RSS sample i, where 0≦i≦N. Theperiod of scanning is a constant value almost all the time. The value ofthe period of scanning, when it is constant, is shown with T₀. Thefunction f(V_(i)T_(i)) is a nonlinear function and generally it is asfollows: For RSS samples which are taken while the scanning device ismoving, the V_(i)T_(i) is considered as the weight of samples. For RSSsamples which are taken while the scanning device is stationary, all thereadings with the same location and the same power reading areconsidered once. For example, if the scanning device collects RSS powersamples while not moving for a given period of time, Tp, and powerreading from an access point was the same for the entire period Tp, onlyone RSS sample from this access point for the period Tp is considered.Finally, RSS samples taken while the scanning device is stationary areconsidered with a correction factor K. The correction factor K can becalculated based on the average acceleration of the scanning device fromzero speed, a₀. Therefore, K=a₀T₀ ².

After removing RSS samples with the same location and power reading(samples taken while the scanning device is stationary) N samplesremain. Of the total number of samples N, if N₁ RSS samples are takenwhile the scanning device is stationary, and if N₂ RSS samples are takenwhile the scanning device is moving, the confidence factor can bewritten as follows:

${CFn} = {{KN}_{1} + {\sum\limits_{i = 1}^{N_{2}}{V_{i}T_{i}}}}$

One example of a confidence factor calculation having this form is asfollows, if scanning period is set to one second:

${CFn} = {{2N_{1}} + {\sum\limits_{i = 1}^{N_{2}}V_{i}}}$

The value of CFn calculated above is an indicator of the reliability ofthe estimation of characteristics of a WLAN access point. Interpretationof the CFn value is as follows. As stated above, a relatively smallnumber of RSS samples will translate to almost no reliability ofestimation, i.e., one or two samples are not enough for a reliableestimate. Increasing the number of RSS samples has an exponential effecton accuracy. In other words, one RSS sample difference at a low numberof samples has a greater impact on accuracy than one RSS sample at ahigh number of samples. On the other hand, when the number of RSSsamples is relatively high, the quality of estimation based on them ishigh. Further increasing the number of RSS samples does not have anoticeable impact on the accuracy of estimation of characteristics, suchas geographic location and radio propagation characteristics, of a WLANaccess point. Therefore, as part of a WLAN access point reliabilitycalculation, there will be two thresholds: CF_(min) is a minimum numberof samples, on average, that are needed to determine a relativelyreliable WLAN access point characteristic estimation. If the number ofRSS samples is below this threshold, the estimation is consideredunreliable. CF_(max) is a threshold beyond which adding extra RSSsamples does not have a significant impact on the accuracy of theestimations.

Since the relationship between confidence factor, CFn, and a reliabilitymeasure, R, of WLAN access point characteristics is logarithmic, thereliability is calculated as follows:

$R = {{\left( {R_{\max} - R_{\min}} \right)\frac{\left\lbrack {{\log \left( {CF}_{n} \right)} - {\log \left( {CF}_{\min} \right)}} \right\rbrack}{\left\lbrack {{\log \left( {CF}_{\max} \right)} - {\log \left( {CF}_{\min} \right)}} \right\rbrack}} + R_{\min}}$

The maximum reliability can be set to one, and the minimum reliabilitycan be set to a very small number. For example,

R_(min)=0.001,

R_(max)=1.

The values of CF_(min) and CF_(max) can be found empirically. Usefulvalues for a general metropolitan WLAN based positioning system are asfollows:

CF_(min)=36,

CF _(max)=68.

According to embodiments of the invention, the reference database [104]can be scaled according to the classifications of the WLAN accesspoints, the quantification of expected error of access point parameters,or quality of associated WLAN access point data. For example, WLANaccess points having a classification or quality measure below a desiredthreshold may be withheld from the reference database [104]. Thisensures that only access points having a relatively high quality ofparameter estimation are used by user device [101] in determining auser's position, speed of travel, or direction of travel. In otherembodiments, all WLAN access points may be included in referencedatabase [104], but the positioning software [103] may not use accesspoints having a classification or quality measure below a desiredthreshold.

According to an embodiment of the invention, another example of scalingthe reference database [104] includes finding a reliability factor foreach WLAN access point in the positioning system, and logged it inreference database [104]. In a WLAN based positioning system, the userhas access to the reference database [104] and uses the WLAN accesspoints in range to estimate its position, speed of travel, and directionof travel. The reliability factor of each WLAN access point in thereference database [104] is used to weight the estimation results of theWLAN access points in range of the user. The use of the reliabilityfactors is independent of the positioning algorithm, which is used toestimate the user's attributes, e.g., position, speed of travel, anddirection of travel. If estimation in the most general form can bewritten as an operation O on function f over WLAN access points AP_(N)in range, O(f(AP₁), . . . , f(AP_(N))), the reliability factor isapplied to the estimation as follows:

O(R₁f(AP₁), . . . , R_(N)f(AP_(N)))

Where N is the total number of access points in range of the user.

WLAN access points with different reliability factors or quality metricscan be combined in different methods. For example, all WLAN accesspoints can be used to estimate the position of a user, but each WLANaccess point is weighted according to its reliability factor. Oneexample is multiplying the estimation results of each WLAN access pointby its reliability, and then combining all results to get the finalestimation result. Another example is to use only the relatively highquality WLAN access points. In this case, WLAN access points areclassified based on their reliability. After detecting all WLAN accesspoints in range, the method starts the estimation process with the WLANaccess points in the highest reliability class. Based on the number ofWLAN access points in the highest class, a decision is made to eitherinclude or exclude WLAN access points in the lower classes. Decidingwhich of these two example methods to use depends on the use case, asdoes the decision to include or exclude lower class WLAN access points.

Another dimension that can be added to the CFn calculation is theaccuracy of the location of RSS samples. The location of RSS samples maybe determined, for example, by a GPS attached to the scanning device. AGPS reports expected Position Error (PE) indicators. The expectedPosition Error of the GPS location estimations can be used to weight RSSsamples as well. Higher weight is given to RSS samples with smallerexpected PE values.

It will be appreciated that the scope of the present invention is notlimited to the above-described embodiments, but rather is defined by theappended claims, and these claims will encompass modifications of andimprovements to what has been described.

1. In a positioning system having a plurality of WLAN access points in a target area, each of the WLAN access points having estimated characteristics used by a location system to provide relative access point weights in a position estimation, the method comprising: determining a quality of an estimation of WLAN access point characteristics for the one or more WLAN access points; and classifying the WLAN access points of the plurality according to the quality of an estimation of WLAN access point characteristics.
 2. The method of claim 1, further comprising: storing the classification of the WLAN access point in a reference database.
 3. The method of claim 1, further comprising: using the classification to quantify an expected error of at least one of the estimated characteristics of the WLAN access point.
 4. The method of claim 1, further comprising: estimating a condition of a WLAN enabled device using the classification of the WLAN access point and at least one of the estimated WLAN access point characteristics.
 5. The method of claim 4, wherein the condition of the WLAN enabled device is at least one of position, speed of travel, and direction of travel.
 6. In a positioning system having a plurality of WLAN access points in a target area, a method for determining a quality of an estimation of WLAN access point characteristics, the method comprising: a WLAN enabled scanning device receiving and measuring a number of received signal strength (RSS) samples of a WLAN signal transmitted by the WLAN access point; estimating the degree to which the measured RSS samples represent the actual coverage area of the WLAN access point; and using the estimation of the degree to which the RSS samples represent the actual coverage area of the WLAN access point to calculate the quality of the estimation of WLAN access point characteristics.
 7. The method of claim 6, wherein estimating the degree to which the RSS samples represent the actual coverage area of the WLAN access point comprises measuring a total distance traveled by the WLAN enabled scanning device while measuring the number of RSS samples.
 8. The method of claim 7, further comprising calculating the quality of the estimation of WLAN access point characteristics based on the total distance traveled by the WLAN enabled scanning device.
 9. The method of claim 6, wherein estimating the degree to which the RSS samples represent the actual coverage area of the WLAN access point comprises measuring the speed of the WLAN enabled scanning device while measuring the number of RSS samples.
 10. The method of claim 9 further comprising assigning a weight to each RSS sample according to the speed of the WLAN enabled scanning device.
 11. The method of claim 6, wherein the WLAN access point characteristics are at least one of geographic location of the WLAN access point and radio propagation characteristics of the WLAN access point.
 12. The method of claim 6, wherein the quality of the degree to which the RSS samples correspond to the actual coverage area of the WLAN access point is based on the number of RSS samples measured.
 13. The method of claim 6, wherein the degree to which the RSS samples correspond to the actual coverage area of the WLAN access point is based on the relative distance between the locations at which each RSS sample is measured.
 14. A method for estimating the position of a WLAN enabled device, the method comprising: identifying WLAN access points within range of the WLAN enabled device; retrieving calculated locations and quality of estimation values corresponding to the identified WLAN access points from a reference database; and estimating the position of the WLAN enabled device using the calculated locations and quality of estimation values.
 15. The method of claim 14, wherein at least one of the quality of estimation values corresponding to at least one of the WLAN access points determines a weight assigned to the calculated location of the corresponding WLAN access point for use in estimating the position of the WLAN enabled device.
 16. The method of claim 14, wherein at least one of the calculated locations corresponding to at least one of the WLAN access points is not used in estimating the position of the WLAN enabled device if the quality of estimation value corresponding to the WLAN access point is below a threshold. 